Triple
T22083821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tunbridge Wells Hospital at Pembury |
E545719
|
entity |
| Predicate | hasSingleRooms |
P146902
|
FINISHED |
| Object | yes |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: yes | Statement: [Tunbridge Wells Hospital at Pembury, hasSingleRooms, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSingleRooms Context triple: [Tunbridge Wells Hospital at Pembury, hasSingleRooms, yes]
-
A.
hasPeriodRooms
Indicates that an entity contains rooms that are decorated or preserved to reflect specific historical periods.
-
B.
hasSingleHall
Indicates that an entity possesses exactly one hall within its structure or domain.
-
C.
hasStateRooms
Indicates that an entity (such as a ship, building, or facility) contains or is equipped with state rooms.
-
D.
hasNumberOfMainRooms
Indicates the relationship that specifies how many main rooms are present in a given entity, such as a building or dwelling.
-
E.
hasRoom
Indicates that an entity possesses, contains, or is associated with a specific room.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e11e3523488190badd54b5d580c00d |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f128b803f081909a0a121aecf526ae |
completed | April 28, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69e71b20ec50819096ac196c798f8e3c |
completed | April 21, 2026, 6:37 a.m. |
| PDg | Predicate description generation | batch_69e7222d208c819098b12c13e31af629 |
completed | April 21, 2026, 7:07 a.m. |
Created at: April 16, 2026, 8:28 p.m.